The Development of an Automated Approach for Designing Quantum Algorithms Using Circuits Generated By Generative Adversarial Networks (Gans) - Sharma, Ankit (Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024)
The advent of quantum computing has inaugurated a novel epoch of computational prowess, offering the potential to tackle intricate problems with unparalleled speed (Zoufal et al., 2019). Quantum circuits, which are essential components of quantum computation, serve as representations of sequences of quantum gates designed for specific quantum processes (Zoufal et al., 2019). Nevertheless, the task of creating efficient quantum circuits continues to be a formidable and labor-intensive undertaking. This research proposal presents a unique methodology that utilizes Generative Adversarial Networks (GANs) to automate the process of generating quantum circuits that are specifically designed for particular quantum gates and operations (Zoufal et al., 2019). The main goal of this study is to create a model based on Generative Adversarial Networks (GANs) that can generate quantum circuits by leveraging the collaborative efforts of the generator and discriminator networks (Zoufal et al., 2019). The GAN model will be trained using a carefully selected dataset that includes established quantum circuits and their corresponding required quantum operations (Zoufal et al., 2019). This dataset will form the basis for the training process. Following this, the quantum circuits that are produced will be thoroughly assessed in terms of fidelity, efficiency, and resource allocation (Zoufal et al., 2019). Additionally, the objective of this work is to refine and optimize the circuits that are formed by employing reinforcement learning and gradient-based techniques (Zoufal et al., 2019). In addition to investigating circuit production, this research will delve into the practical implications and consequences of quantum circuits formed by Generative Adversarial Networks (GANs) on the development of quantum algorithms (Zoufal et al., 2019). The study's value is in its capacity to accelerate the creation of quantum algorithms through the automation of circuit design (Zoufal et al., 2019). This research makes a valuable contribution to the field of quantum computing by improving the efficiency and resource utilization of quantum circuits (Zoufal et al., 2019). These developments are crucial for the development of practical quantum computing applications and will play a significant role in the evolution of quantum algorithms and processing capabilities in the future (Zoufal et al., 2019).